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Kathy Barthelt

Infor ERP Tips & Tricks: Gain Insight into Inventory Variances with Cycle Counting Analytics

Applies to: Infor LN Baan environments

Why this matters

Inventory discrepancies are often identified, but not fully understood. Without clear visibility into where and why variances occur, teams are left reacting instead of improving processes.

What this solves

The Cycle Counting Variances session (whinh8352m000) provides a visual way to analyze inventory discrepancies, helping teams identify patterns and root causes across warehouses and counting activities.


How it works

This session displays interactive bar charts that show:

  • Cycle counting variances by warehouse (default view)
  • Cycle counting variances by reason (drill-down view)

Users can:

  • Hover over bars to view variance details
  • Click into a warehouse to analyze variance reasons
  • Zoom and filter chart views for deeper analysis

Chart settings can be adjusted using the Cycle Counting Variances – Chart Settings session (whinh0901m500).

Variance values are calculated based on the valuation method defined in Inventory Revaluation Transactions (whina1522m000).

Key benefits

  • Visual insight into where variances are occurring
  • Root cause analysis by warehouse and reason
  • Improved inventory control through better understanding of discrepancies
  • Faster decision-making with interactive data views

Practical insight

Identifying a variance is only the first step. Understanding patterns—such as recurring issues by warehouse or reason—is what enables teams to reduce discrepancies over time.

When combined with NextTrack Insights dashboards, this data can be extended to:

  • Track variance trends over time
  • Highlight recurring problem areas
  • Provide real-time visibility beyond standard sessions

Getting started

The Cycle Counting Variances session can be accessed from the menu or your Role-Based Home Page. Crossroads RMC can help you extend this visibility with dashboards that bring together inventory, variance, and operational data into a single, actionable view.

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Kathy Barthelt

Kathy BartheltKathy Barthelt

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